2134/37229 Yang Zhou Yang Zhou Jiangtao Wang Jiangtao Wang Baihua Li Baihua Li Qinggang Meng Qinggang Meng Emanuele Rocco Emanuele Rocco Andrea Saiani Andrea Saiani Underwater scene segmentation by deep neural network Loughborough University 2019 untagged Information and Computing Sciences not elsewhere classified 2019-03-18 14:48:59 Conference contribution https://repository.lboro.ac.uk/articles/conference_contribution/Underwater_scene_segmentation_by_deep_neural_network/9405458 A deep neural network architecture is proposed in this paper for underwater scene semantic segmentation. The architecture consists of encoder and decoder networks. Pretrained VGG-16 network is used as a feature extractor, while the decoder learns to expand the lower resolution feature maps. The network applies max un-pooling operator to avoid large number of learnable parameters, and, in order to make use of the feature maps in encoder network, it concatenates the feature maps with decoder and encoder for lower resolution feature maps. Our architecture shows capabilities of faster convergence and better accuracy. To get a clear view of underwater scene, an underwater enhancement neural network architecture is described in this paper and applied for training. It speeds up the training process and convergence rate in training.